Secure models for model-based control and optimization

ABSTRACT

In certain embodiments, a control/optimization system includes an instantiated model object stored in memory on a model server. The model object includes a model of a plant or process being controlled. The model object comprises an interface that precludes the transmission of proprietary information via the interface. The control/optimization system also includes a decision engine software module stored in memory on a decision support server. The decision engine software module is configured to request information from the model object through a communication network via a communication protocol that precludes the transmission of proprietary information, and to receive the requested information from the model object through the communication network via the communication protocol.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 15/051,372, which was filed on Feb. 23, 2016, whichis a continuation of and claims priority to U.S. patent application Ser.No. 13/669,165, filed on Nov. 5, 2012, the entire contents of which areherein incorporated by reference.

BACKGROUND

The present disclosure relates generally to control systems and, moreparticularly, the utilization of secure models for model-based controland optimization in control systems.

The overwhelming success of model-based optimization and control in allaspects of modern life (aerospace and transportation, materials andprocessing, biology and medicine, robotics, information and networks,and so forth) has given mathematical modeling a critical role in allfields of engineering and physics. Conventional model-based optimizationand control solutions typically assume full disclosure of the model tothe decision engine. This full disclosure can be undesirable if themodel contains sensitive information that the owner of the model is notwilling to disclose. Currently, the only remedy to safeguard thesensitive content of the model is for the owner of the model to assumefull ownership of the entire optimization and/or control solution.

BRIEF DESCRIPTION

In an embodiment, a computer-implemented method includes instantiating aplurality of model objects on a plurality of model servers. Each of theplurality of model objects includes a model of a plant or process beingcontrolled. At least one of the models contains protected information.The method also includes requesting information from the plurality ofmodel objects via a communication network. The method further includesreceiving the information from the plurality of model objects via thecommunication network. In addition, the method includes generating anapproximation of at least one of the plurality of the model objectsrelevant to generating control commands. The method also includesgenerating control commands based at least in part on the approximationand the information received from the plurality of model objects. Themethod further includes transmitting the control commands to at leastone of the plurality of model objects via the communication network. Inaddition, the method includes controlling an industrial automationcomponent based on the control commands.

In another embodiment, a control/optimization system includes aplurality of instantiated model objects stored in memory on a pluralityof model servers. Each of the plurality of model objects includes amodel of a plant or process being controlled. The control/optimizationsystem also includes a plurality of decision engine software modulesstored in memory on a plurality of decision support servers. Each of thedecision engine software modules includes software instructions for:requesting information from the plurality of model objects via acommunication network; receiving the information from the plurality ofmodel objects via the communication network; generating control commandsbased at least in part on the information received from the plurality ofmodel objects, wherein the control commands are generated collectivelyby the plurality of decision engine software modules; and transmittingthe control commands to at least one of the plurality of model objectsvia the communication network.

In another embodiment, a control/optimization system includes aninstantiated model object stored in memory on a model server. The modelobject includes a model of a plant or process being controlled. Themodel object comprises an interface that precludes the transmission ofproprietary information via the interface. The control/optimizationsystem also includes a decision engine software module stored in memoryon a decision support server. The decision engine software module isconfigured to request information from the model object through acommunication network via a communication protocol that precludes thetransmission of proprietary information, and to receive the requestedinformation from the model object through the communication network viathe communication protocol.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic diagram of an exemplary commercial or industrialenergy system that may be controlled;

FIG. 2 is a block diagram of exemplary components of the energy systemof FIG. 1, illustrating various interconnections;

FIG. 3 is a block diagram of an exemplary parametric hybrid model formodeling the energy system of FIG. 1;

FIG. 4 is a block diagram of an exemplary evaporation chiller block ofFIG. 2;

FIG. 5 is a block diagram of an exemplary boiler block of FIG. 2;

FIG. 6 is an example of a graphical user interface (i.e., a graphicalrepresentation) of a graphical modeling tool representing a plurality ofparametric hybrid models relating to components of the system of FIG. 1arranged as a network;

FIG. 7 is a block diagram of an enterprise-integrated parametric hybridmodel enabled control system for controlling the system of FIG. 1;

FIG. 8 is an example of the graphical user interface (i.e., a graphicalrepresentation) of the graphical modeling tool illustrating a library ofcomponent blocks available to a user;

FIG. 9 is an example of the graphical user interface (i.e., a graphicalrepresentation) of the graphical modeling tool illustrating anoptimization view when an Optimization tab is selected by the user;

FIG. 10 is an example of the graphical user interface (i.e., a graphicalrepresentation) of the graphical modeling tool illustrating theoptimization view when the user has submitted a command input and theoptimization solution of the system of FIG. 1 has been updated;

FIG. 11 is an example of a non-linear and non-convex optimizationproblem and two convex approximations for the problem;

FIG. 12 is an example of a solution graph for optimization solutionequations using the parametric hybrid models;

FIG. 13 is an example of a method for utilizing the graphical userinterface to interact with the parametric hybrid models;

FIG. 14 is an example of the graphical user interface of the graphicalmodeling tool illustrating a model network being optimized based on afirst node;

FIG. 15 is an example of the graphical user interface of the graphicalmodeling tool illustrating the model network of FIG. 14 being optimizedbased on a second node that has been selected by a user;

FIG. 16 is a block diagram of a distributed enterprise-integratedparametric hybrid model enabled control/optimization system; and

FIG. 17 is a block diagram of the interfaces of parametric hybrid modelobjects and associated protocol that is used to communicate between adecision engine and the parametric hybrid model objects.

DETAILED DESCRIPTION

As discussed above, the overwhelming success of model-based optimizationand control in all aspects of modern life (aerospace and transportation,materials and processing, biology and medicine, robotics, informationand networks, and so forth) has given mathematical modeling a criticalrole in all fields of engineering and physics. Models of physicalprocesses may be broadly categorized as first-principles(phenomenological, physical, mechanistic) or empirical (statistical,data-centric). A first-principles (FP) model commonly consists of a setof equations describing known relationships among the variables, withcoefficients or other parameters that may be fitted to data. Empiricalmodels presume no particular form for the relationships, and instead fitinput-output models from data alone. Neural network (NN) models, whichemploy a large number of parameters in a universal approximationstructure, are one of the most widely used forms of nonlinear empiricalmodeling methods due to their many favorable properties.First-principles have historically dominated nonlinear process modeling.The advent of NNs in the mid-1980s made it possible to obtainwell-behaved nonlinear empirical models.

While the complementary strengths and weaknesses of the two modelingapproaches were widely recognized, and the value of an approach thatallows for their strengths to complement one another is generallyaccepted, it was only recently that a systematic methodology to buildmodels using complementary strengths of both first-principles andempirical approaches was developed. One such approach is described ingreater detail in U.S. Pat. Nos. 8,019,701, 8,032,235, and U.S. PatentApplication Publication No. 2005/0187643, each of which is incorporatedherein by reference in its entirety for all purposes.

Regardless of the methodology by which the models are developed, asdiscussed above, conventional model-based optimization and controlsolutions typically assume full (or at least partial) disclosure of themodel to the decision engine (e.g., the quadratic programming (QP)solver at the center of a model predictive control (MPC) solution, themixed integer linear programming (MILP) solver at the center of aproduction scheduling solution, and so forth). This full disclosure canbe undesirable if the model contains sensitive information that theowner of the model is not willing to disclose. Currently, as discussedabove, the only remedy to safeguard the sensitive content of the modelis for the owner of the model to assume full ownership of the entireoptimization and/or control solution.

This option, however, has several drawbacks. For example, using suchtechniques may lead to the model-based optimization and control solutionbeing too expensive for end-users with limited on-site expertise. Forexample, a manufacturing plant often cannot support a full-time positionfor an optimization expert and, hence, may be forced to rely on outsideexpertise to support their optimization/control application. Obtainingexternal assistance for maintenance and troubleshooting of a deployedmodel-based solution is relatively more expensive. In a worst casescenario, this cost may be prohibitive to the point of abandoning aninstalled solution. Furthermore, the need to protect proprietaryinformation only increases the cost of maintenance as the plant has totake extra steps to safeguard valuable information. For example, a plantmay require a consultant to forego the possibility of working for theircompetitors, which undoubtedly increases the cost.

In addition, conventional techniques for handling security of models mayeven lead to internal exposure of highly valued proprietary informationbeing expanded unnecessarily. For example, a plant control engineer mayhave access to the detailed reaction model even though he/she does notnecessarily need access to that level of detail. In addition, any notionof collaborative decision making between different entities (e.g.,collaborative supply-chain optimization) may be hindered by concernsover disclosure of proprietary information. For example, pricing modelsare some of the most highly guarded information for a company, and yeteffectively participating in a true supply chain optimization solutioninvolving different tiers of supply chain generally requires accuratepricing strategy from all participants. As a consequence, the use of acloud as a platform for decision support is hindered by concerns overownership and security of the proprietary data.

A solution that addresses the ownership and security concerns in afundamentally sound manner will be the cornerstone of any meaningfuldecision-support solution through cloud infrastructure. The embodimentsdescribed herein include a methodology and algorithms for addressing theabove listed challenges. More specifically, the embodiments describedherein treat models as secure servers that provide services to clients.In certain embodiments, the decision engine (e.g., optimization andcontrol engine) creates an approximation of the protected model byinquiring mutually agreed upon model properties without ever accessingmodel details. The decision engine may determine optimal values fordecision variables based on the online approximation that is created. Inaddition, in certain embodiments, feedback from the implications of thedeployed decisions may be used to modify the online approximations asneeded. Furthermore, in certain embodiments, the properties of theapproximate online model may be reported back to the protected modelserver in order to provide the authorized owners of the model withnecessary feedback for maintaining/modifying the models as needed. Incertain embodiments, queries to the models may be encrypted to furtherprotect the protected model information from unauthorized disclosure.The embodiments described herein are generally presented as relating tothe manufacturing industry and utility systems. However, the systems andmethods described herein are not limited to such applications, and mayindeed be extended to any and all applications where protection ofsystem modeling information is desirable.

The embodiments described herein enable model-based computations,model-based optimization and control in particular, to be carried outwhile keeping part of or the entire model content secure. In particular,at no point will content of a model deemed to be proprietary bedisclosed to the computational engine that utilizes that model in itscomputations. As such, the embodiments described herein arefundamentally different from conventional systems and methods thatsecure access to model content via passwords, for example. To enablethis objective, the embodiments described herein include a softwareimplementation methodology that will fundamentally change the waymodel-based computations (e.g., model-based optimization and modelpredictive control) are carried out today.

The embodiments described herein may prove beneficial for myriadapplications. For example, one such application is in the realm ofplanning and scheduling of operations in an industrial plant. Complexapplications such as providing steam, chilled water, and electricity toa sophisticated user (e.g., a petrochemical complex, a universitycampus, a large residential complex, and so forth) involves constantdecisions by plant operation personnel on which resources to utilize,what the set points for that resource (e.g., capacity) should be, forhow long that resource must operate, and what existing or impendingconstraints must be avoided and how. The complexity of decision-makingin such applications justifies the need for a systematic optimizationsolution in which various components of the system, operationalprocedures and constraints, economic objectives, and so forth, areproperly modeled. Even within a single industrial plant, the plant maydecide to avoid disclosing their operational procedures to a firmproviding automation solutions as that very firm may deliver a solutionto their competitors. The embodiments described herein enablesafeguarding of the operation procedures in such circumstances, forexample.

Another exemplary application includes optimizing product compositionsgiven acceptable recipe alternatives. Many manufacturing operationsinvolve producing an end-product for which an acceptable quality can bereached following two or more recipe alternatives (e.g., powdered cheesein a dairy plant). A principled approach to economic optimization of theproduction path requires that the decision engine have access to therecipe options. Often, product recipes are the most guarded proprietaryinformation for a company and, hence, the ability to avoid disclosure ofsuch recipes is viewed as an enabling technology for deploying adecision support solution.

Another exemplary application includes multi-unit optimization in anindustrial plant. Complex processes ranging from powder milk drying in adairy plant to boiler operation in a power plant are often multi-unitoperations that can benefit from a principled optimization strategy toimprove the energy efficiency of normal operations, reduce the cost ofresponse to process disturbances, improve their ability to respondprofitably to the changes in market conditions, and so forth. Addressingproprietary information in the models that are used in a systematicoptimization solution in this scenario is a well-recognized need.

Another exemplary application includes multi-plant optimization in amanufacturing enterprise. An extension of the multi-unit optimization,the economic optimization of multiple plants (e.g., within amanufacturing enterprise) may impose even more requirements onprotecting model contents and, hence, the embodiments described hereinwill be an enabling technology for optimal coordination of multipleproduction plants.

Another exemplary application is supply chain optimization in amanufacturing enterprise. This is a further extension to the multi-plantoptimization scenario described above. In a supply chain, the issue ofprotecting model content (e.g., the supplier's pricing strategy forcomponents provided to a manufacturing facility) receives higherprominence for obvious business reasons. The embodiments describedherein enable a true supply chain optimization solution.

Another exemplary application includes optimizing buy/sell decisions inan enterprise. An example of such buy/sell decisions is the decision ofa utility plant to purchase gas, coal, biofuel, and electricity from thegrid, with the potential of selling steam, chilled water, and evenelectricity back to various customers. Such decisions are growing incomplexity and hence a systematic model-based optimization tool willbecome increasingly indispensable. As an example, the current trend insmart grids (e.g., where each node on the electric grid can perform asboth source and sink) adds further complexity to the decision makingprocess. The embodiments described herein are an enabling technology forsuch applications.

There is often a discontinuity between the generally offline (i.e., notduring operation) planning and scheduling activities of a plant and thegenerally online (i.e., during operation of the plant) control andoperation activities of the plant. The embodiments described hereinaddress the three main challenges that have contributed to thepersistence of this deficiency. First, the embodiments described hereinprovide a versatile modeling framework for representing an entire plantand, indeed, an entire enterprise including one or more plants. Existingmodeling frameworks are generally unable to: (a) capture relevantdetails of plant operation as it pertains to economic objectives of theenterprise, (b) avoid prohibitive complexity given the number ofcomponents to be included in the models that represent the plants, and(c) maintain modularity such that there is an intuitive correspondencebetween the components of the physical plants/processes and the modelcomponents. Certain embodiments described herein address thesechallenges by employing a parametric hybrid modeling framework, such asdescribed in greater detail in U.S. Pat. Nos. 8,019,701, 8,032,235, andU.S. Patent Application Publication No. 2005/0187643, each of which isincorporated herein by reference in its entirety for all purposes.However, it should be noted that the techniques described herein may beextended to other types of modeling frameworks whereby models of theplant or process being controlled are utilized.

Second, the embodiments described herein address the conventionalseparation of the offline interactions with the models (e.g., modelbuilding, planning, scheduling interactions) that represent the plants,and the online interactions (e.g., the control and operationinteractions) with the models. In particular, in conventional systems,the deployed models are not transparent to all users. In other words,the quality of the models and their components are not easily measurableor accessible as the models are deployed to an online environment. Inthese conventional systems, modification of the models is generally anoffline exercise, and the expertise for modifying the models isgenerally highly centralized. However, in reality, the people who arequalified to modify one component of a model may have no qualificationto modify another component of the model, and these different peopleoften physically reside in different locations. Generally speaking,asynchronous modification of the model components is not possible, andmodification frequency widely varies depending on the model type,operation scenario, and so forth. The embodiments described hereinaddress these challenges by employing a transparent model deploymentstrategy while also maintaining security of proprietary informationrelated to such models.

Third, the embodiments described herein provide a graphical optimizationlanguage that eliminates the communication barrier between optimizationsoftware and end users (plant operators, accounting department,financial department, and so forth). In particular, the graphicallanguage for optimization enables a lower level of competency toimplement and/or deploy optimization solutions. In other words, ratherthan requiring a Ph.D. with an optimization background, a plant managerwith process knowledge will be able to own the optimization solution. Inaddition, the graphical language provides distributed development,deployment, and maintenance capabilities such that the composition ofthe optimization problem and subsequent modifications to theoptimization problem may be carried out with input from relevantstakeholders in their normal operation settings.

The embodiments described herein enable the handling of various aspectsof operation (e.g. accommodation of scheduled maintenance for keycomponents, robustness with respect to disruptions in the supply chainor available capacity, energy efficiency and low environmental footprintof the operation, responsiveness to market pricing pressures, and soforth) in a systematic manner with full transparency of the objectives,priorities, and constraints of the underlying models, but also with thecapability of securing proprietary information. In particular, theembodiments described herein enable graphical setup, execution, andreporting of large-scale (potentially non-linear) optimization problemsin a manner that the plant-wide and/or enterprise-wide optimizationsolutions may be simultaneously managed by a distributed set ofstakeholders without the need for a centralized authority to act as agatekeeper of the information and transactions. To achieve thisobjective, the embodiments described herein include core enablingalgorithmic concepts, as well as a software implementation methodology.

As described above, the embodiments described herein have many potentialapplication scenarios. For example, the embodiments described hereinfacilitate improved planning and scheduling of operations in anindustrial plant. Complex applications such as providing steam, chilledwater, and electricity to complex energy users (e.g., petrochemicalcomplexes, university campuses, large residential complexes, and soforth) involve constant decisions by plant operation personnel, such aswhich resources should be utilized, what set points for the resources(e.g., capacity) should be set, for how long the resources shouldoperate, what existing or impending constraints should be avoided, andso forth. The complexity of the decision making in such applicationsjustifies the need for a systematic optimization solution, but thechallenges described above have heretofore impeded the development of afully functional solution.

In addition, the embodiments described herein facilitate theoptimization of product compositions given acceptable recipealternatives. Many manufacturing operations involve producing endproducts that may be reached via alternative recipes (e.g., cheesemanufacturing in a dairy plant). The embodiments described hereininclude a principled approach to optimal scheduling of the manufacturingprocess such that, at any given time, the end product having apredetermined quality specification is made with the optimal set ofingredients.

Furthermore, the embodiments described herein also facilitate multi-unitoptimization in an industrial plant. Complex processes ranging frompowder milk drying in a dairy plant to boiler operation in a power plantare inherently multi-unit operations that may benefit from a principledoptimization strategy to improve, for example, the energy efficiency ofoperation, reduce the cost of response to process disturbances, improvethe ability to respond profitably to changes in market conditions, andso forth.

The embodiments described herein also facilitate optimizing buy and/orsell decisions for an industrial plant on an electric grid. Many largeconsumers of electricity, such as industrial plants or universitycampuses, have in-house generation capacity. The economics of thein-house generation versus purchase from an electric grid is growingincreasingly more complex as utility companies move away from fixedpricing in order to maximize their profitability. The current trend insmart grids, where each node on the electric grid may perform as bothsource (i.e., provider of power) and sink (i.e., consumer of power),further complicates the decision making process. A principledoptimization solution may assist such customers to make the mostfavorable decisions at any given time given their priorities andobjectives.

The embodiments described herein include several aspects that enable theapplications described above. For example, the embodiments describedherein provide online transparency to model quality and performancewhile also maintaining security of proprietary information. Without theability to investigate model quality (both for individual units, and fora network built using these units), model fidelity may not be sustained.For example, with a purely empirical modeling paradigm, it may not bepossible to pinpoint a source of quality deterioration and, hence,online visibility of the models may not be fully achieved. A detailedfirst-principles based model may suffer from this lack of transparency.In addition, the ability to modify a targeted component of a deployedmodel without forcing deactivation of the model is highly desirable. Theonline modification of the transparent models in this embodimentincludes and surpasses that of parameter adaptation, and encompasses theinclusion of a new parameterized model to replace an earlierunderperforming parameterized model. Therefore, the online transparencydescribed herein generally improves model quality and performance.

In addition, the embodiments described herein provide for asynchronousauthoring capability for the problem formulation by a distributed set ofusers. The large scale of the optimization problem, and the limitedscope of responsibility and competency for plant operators andengineers, makes distributed asynchronous authoring of the problemstatement desirable (and often necessary). For example, in a utilityplant, a chilled water loop and a steam loop are operationally coupled.The experts that understand the chilled water loop generally know verylittle about the steam loop operation, and most likely are not allowedand/or do not want to assume responsibility for the operation of thesteam loop, and vice versa. The distributed authoring capability shouldalso apply to the outcome of the optimization solution. The outcome ofthe plant-wide and/or enterprise-wide optimization solution (e.g., aGantt chart of operation schedules for chillers of a utility plant) ispresentable to a distributed set of users (e.g., operators, plantmanagers, and so forth). In addition, authorized stakeholders areenabled to edit proposed schedules without creating inconsistencies.Furthermore, the distributed users are enabled to update operationalconstraints and request rescheduling in a consistent manner.

The embodiments described herein also provide graphical authoringcapabilities for the problem formulation by the distributed set ofusers. Without graphical editing capability, a typical plant operatorwould not be able to directly contribute to model maintenance. Inaddition, without a graphical language for defining the optimizationproblem or interpreting the solver decisions, a typical plant operatoror engineer would not be able to contribute to a meaningful definitionof the optimization problem. The graphical authoring capabilitydescribed herein also applies to the outcome of the overall optimizationproblem. The outcome of the plant-wide and/or the enterprise-wideoptimization solution (e.g., a Gantt chart of operation schedules forchillers) is presentable to the distributed set of users (e.g.,operators, plant managers, and so forth). The authorized stakeholdersmay graphically edit the proposed schedules without creatinginconsistencies. In addition, the distributed set of users maygraphically update operational constraints and request rescheduling in aconsistent manner. The intuitiveness of the graphical authoringcapability enhances usability and uptime of the optimization solution.

In addition, the embodiments described herein incorporate real-timemeasurements and information from the plant floor and/or businesssystems. In a plant-wide and/or enterprise-wide optimization, thenetwork is often composed of a large number of component models, complexnetwork connectivity, and a dynamic set of operational conditions,constraints, and objectives. The information needed to keep this“problem formulation” up-to-date is obtained from sources that aredistributed throughout the enterprise, and often function with localautonomy. A solution that requires centralized information handling maybecome untenable. In particular, real-time measurements influence themodels in the problem formulation (e.g., efficiency curves often changebased on the current operating condition of the equipment). The abilityto achieve integration with real-time measurements can be an obstacle tothe successful adoption of plant-wide optimization solutions. Modeltransparency facilitates successful incorporation of real-timeinformation as the changes may be viewed by all relevant stakeholders.

Turning now to the drawings, FIG. 1 is a schematic diagram of anexemplary commercial or industrial energy system 10. As described above,the energy system 10 of FIG. 1 is an example of the types of plants thatmay benefit from the embodiments described herein. FIG. 1 illustratesthe various energy generation and consumption components that aretypical in commercial and industrial energy systems. For example, FIG. 1includes boilers 12 that are configured to receive fuel and generatesteam for use as a source of power in other components of the energysystem 10. For example, in certain embodiments, the steam produced bythe boilers 12 may be used by cogeneration units 14 to drive generators16, which generate electrical power that may be consumed by componentsof the energy system 10 and/or sold to an electrical grid 18. Inaddition, in certain embodiments, a heat recovery steam generation(HRSG) system 20 may be used for secondary recovery of heat throughgeneration of steam, which may also be used to drive generators 16 forgenerating electrical power. In addition to selling electricity to thegrid 18, the energy system 10 may also buy electricity from the grid 18.Whether the energy system 10 buys from or sells to the grid 18 at anyparticular point in time depends on the current electricity supply ofthe energy system 10, the current electricity demand of the energysystem 10, electrical storage capacity of the energy system 10, buy/sellprices to and from the grid 18, day/night cycles of the energy system10, the availability and capacity of other generation systems connectedto the grid 18, and so forth.

As illustrated, the energy system 10 may include process units 22 andbuildings 24 that consume some of the electrical power, chilled water,and/or steam. In addition, in certain embodiments, the energy system 10may include electric chillers 26 and steam chillers 28, which may beassociated with a thermal energy storage tank 30, and may consume energyto generate chilled water, which may be pumped to the process units 22and buildings 24 by pumps 32 for cooling, such as for building cooling,industrial process cooling, and so forth. In addition, heated waterfrom, for example, the chillers 26, 28 may be circulated through acooling tower 34 and associated heat exchangers 36 and pumps 38, wherethe heated water is cooled for later use.

Therefore, in summary, various components may produce energy (i.e.,referred to as sources) and/or consume energy (i.e., referred to assinks) in a typical commercial or industrial energy system 10. Indeed,the components shown in FIG. 1 are merely exemplary of the componentsthat may comprise a typical commercial or industrial energy system 10.As illustrated in FIG. 1, the various components of the energy system 10may be configured to consume and/or produce energy based upon differenttechnologies. The interdependence of the components of the energy system10 may, in certain embodiments, be extremely complex. In addition,various external components, such as the electrical grid 18 may add tothe complexity of the energy system 10. Again, the energy system 10illustrated in FIG. 1 is merely exemplary of the types of complex plantsand enterprises that may utilize the graphical modeling frameworkdescribed herein.

FIG. 2 is a block diagram of exemplary components of the energy system10 of FIG. 1, illustrating various interconnections. In particular, FIG.2 depicts various energy loops that are typical in commercial andindustrial energy systems 10. For example, key energy loops include afuel loop 40, an electric loop 42, a condenser loop 44 (e.g., coolingtower water), an evaporator loop 46 (e.g., chiller water), and a steamloop 48. The various energy loops 40, 42, 44, 46, 48 illustrated in FIG.2 are merely exemplary and not intended to be limiting. In otherembodiments, other energy loops may be used to model the energy system10.

Each energy loop 40, 42, 44, 46, 48 includes a set of defining variablesthat function as inputs and outputs for the respective energy loop 40,42, 44, 46, 48. For example, the fuel loop 40 includes t^(G), p^(G),f^(G), and r, where t^(G) is the fuel temperature, p^(G) is the fuelpressure, f^(G) is the fuel flow rate, and r is the heat factor for thefuel loop 40. The electric loop 42 includes kw, which is the amount ofelectricity supplied. The condenser loop 44 includes ts^(C), tf^(C), andf^(C), where ts^(C) is the temperature of the water entering the coolingtower(s), tf^(C) is the temperature of the water exiting the coolingtower(s), and f^(C) is the flow rate for the water in the condenser loop44. The evaporator loop 46 includes ts^(E), tf^(E), and f^(E), wherets^(E) is the temperature of the chilled water leaving the chillers,tf^(E) is the temperature of the chilled water returning to thechillers, and f^(E) is the chilled water flow rate. The steam loop 48includes t^(S), p^(S), and f^(S), where t^(S) is the steam temperature,p^(S) is the steam pressure, and f^(S) is the steam flow. Again, all ofthe variables for the energy loops 40, 42, 44, 46, 48 illustrated inFIG. 2 are merely exemplary and not intended to be limiting. In otherembodiments, other variables may be used to define the energy loops 40,42, 44, 46, 48.

As illustrated, the energy loops 40, 42, 44, 46, 48 are coupled tocomponent blocks, which represent groups of actual energy-relatedequipment of the energy system 10 that typically supply energy to orconsume energy from the energy loops 40, 42, 44, 46, 48. For example, aboiler block 50 is coupled to both the fuel loop 40 and the steam loop48, an electrical generator block 52 is coupled to the fuel loop 40, theelectric loop 42, and the steam loop 48, an evaporation chiller block 54is coupled to the electric loop 42, the condenser loop 44, and theevaporator loop 46, and an absorption chiller block 56 is coupled to theevaporator loop 46 and the steam loop 48. Again, the various componentblocks 50, 52, 54, 56 illustrated in FIG. 2 are merely exemplary and notintended to be limiting. In other embodiments, other component blocksmay be coupled to the various energy loops 40, 42, 44, 46, 48.

The disclosed embodiments facilitate both planning/scheduling andcontrol/operation of the energy system 10 of FIGS. 1 and 2. Morespecifically, as described in greater detail below, the embodimentsdescribed herein include a graphical language and interface andtransparent modeling framework for the energy system 10 of FIGS. 1 and 2that enables different sets of distributed users having widely differentareas of expertise to interact with parametric hybrid models for theindividual component blocks (e.g., groups of equipment) of the energysystem 10. Indeed, it should be understood that while the embodimentsdescribed herein are presented as relating to energy-efficient operationof energy systems 10, in other embodiments, the graphical language andinterface and transparent modeling framework of the embodimentsdescribed herein may be extended to other applications, such as chemicalmanufacturing, oil and gas processing, and so forth.

The disclosed embodiments target optimization of the energy system 10 ofFIGS. 1 and 2 that addresses the computational complexity challenge ofmodeling the many various energy-related components of the energy system10, including individual parametric hybrid models for generation units,boilers, chillers, pumps and fans, and so forth, as well as parametrichybrid models for constraints and objectives. In addition, the disclosedembodiments provide for online modification of model structure and/orparameters by the different sets of distributed users via a graphicallanguage and interface and transparent modeling framework whilemaintaining security of proprietary information.

Parametric objective functions may be built to reflect the economicobjectives of the operation of the energy system 10. A parametricconstraint set may be built to reflect constraints of the operation ofthe energy system 10 (e.g. constraints on cooling capacity, constraintson allowable emissions, and so forth). As described in greater detailbelow, the graphical language described herein enables all stakeholdersin the energy system 10 to interact with the parameters of theparametric hybrid models, the parametric objective functions, and theparametric constraint sets, even if access to the underlying parametrichybrid models are limited to particular users (e.g., modeling experts).Energy load models may also be built to predict load profiles over anoperation time horizon. The load models may include, for example,chilled water demand, steam demand, electricity demand, and so forth.Based on all of these models and objectives, the optimization problemfor the energy system 10 may then be solved to determine the optimalprofile for the operating conditions of the energy system 10, subject tothe parametric constraint set.

Because of the complexity of typical commercial and industrial energysystems 10, the hybrid techniques described herein provide uniqueadvantages. Hybrid techniques leverage known fundamental relationships(e.g., known kinetic models, and so forth) that are more or lessavailable from fundamental process modeling with empirical modelingtechniques for phenomena not accurately modeled due to a lack offundamental understanding. Because industrial-scale energy equipment isgenerally uniquely designed and developed for intensive operations,significant calibration or tuning of published or available fundamentalmodeling with specifically-designed empirical modeling techniquesprovides more accurate energy models. In turn, a more accurate energymodel enables a more highly performing model-based optimization andcontrol solutions. Therefore, an ideal modeling solution incorporatesthe best available fundamental models and empirical models tuned orcalibrated to best match collected energy equipmentmeasurement/performance data over varying operating phases of the energysystem 10. Depending on the accuracy of the parametric hybrid models,either linear (e.g. single value) parameters or nonlinear (e.g. kineticparameters that vary with measured energy) variables may be identifiedand used.

FIG. 3 is a block diagram of an exemplary parametric hybrid model 58 formodeling the energy system 10 and/or, more particularly, individualcomponent blocks 50, 52, 54, 56 of the energy system 10. As illustrated,energy variable inputs u_(k) from the energy system 10 may be receivedby the parametric hybrid model 58. The energy variable inputs u_(k) may,for example, include the variables of the energy loops 40, 42, 44, 46,48 described above. An empirical model 60 may use the energy variableinputs u_(k) to generate empirical model outputs w_(k). The empiricalmodel outputs w_(k) may be a function of the energy variable inputsu_(k) and empirical model parameters ρ. Both the empirical model outputsw_(k) and the energy variable inputs u_(k) may be directed into aparameter model 62 of the parametric hybrid model 58. Fundamental modelparameters θ_(k) from the parameter model 62 may be a function of theenergy variable inputs u_(k) and the empirical model outputs w_(k). Itshould be noted that both the length of the fundamental model parametersθ_(k) and the value of the parameter vector may vary as a function ofthe energy variable inputs u_(k) and the empirical model outputs w_(k).In certain embodiments, the fundamental model parameters θ_(k) mayinclude the empirical model outputs w_(k), or may simply be identical tothe empirical model outputs w_(k) in their simplest form. Thefundamental model parameters θ_(k) may be directed into a parametricfirst-principles model 64, which may be either a steady-state or dynamicmodel. In addition, the parametric first-principles model 64 may receivethe energy variable inputs u_(k) from the energy system 10. Theparametric first-principles model 64 may model measured or unmeasuredenergy state variables x_(k) and energy variable outputs y_(k). Theenergy state variables x_(k) may be a function of the energy variableinputs u_(k), previous energy state variables x_(k), and the fundamentalmodel parameters θ_(k). The energy variable outputs y_(k) may be afunction of the energy variable inputs u_(k), current energy statevariables x_(k), and the fundamental model parameters θ_(k). The energyvariable outputs y_(k) may be directed from the parametric hybrid model58 as outputs. Therefore, the general equations defining the parametrichybrid model 58 include:w _(k) =f ₁(u _(k),ρ);θ_(k) =f ₂(u _(k) ,w _(k));x _(k) =F _(k)(u _(k) ,x _(k-1),θ_(k)); andy _(k) =G _(k)(u _(k) ,x _(k),θ_(k));

where u_(k) is a vector of energy variable inputs over time k, ρ is avector of empirical model parameters, w_(k) is a vector of empiricalmodel outputs over time k, θ_(k) is a vector of fundamental modelparameters over time k, x_(k) is a vector of measured or unmeasuredenergy state variables over time k, and y_(k) is a vector of energyvariable outputs over time k.

The parametric hybrid model 58 is extremely efficient for real-timeoptimization and control computations. This computational efficiency iscritical to the successful implementation of a model-based optimizationand control strategy that optimizes the performance of the energy system10. Dynamic optimization methods are used to calculate optimal dynamictrajectories during operation of the energy system 10 to optimize theefficiency of the energy system 10 as a whole. In particular,trajectories may be calculated for individual components of thecomponent blocks 50, 52, 54, 56 of the energy system 10 and optimized toa target over time based on parameters that are closely related to, butare not the same as, the input and output variables which are listedabove as being associated with the various energy loops 40, 42, 44, 46,48. More specifically, as illustrated in FIG. 3, the fundamental modelparameters θ_(k) generated by the parameter model 62 may be a set ofparameters that are not directly analogous to either the energy variableinputs u_(k) or the energy variable outputs y_(k). Rather, certainderived measures (e.g., the parameters) of the energy system 10 over thecourse of operation of the energy system 10 may be used to generatetrajectories that strongly correlate to performance variables for theenergy system 10, even when the performance variables for the energysystem 10 are not directly measurable.

For example, the efficiency of a boiler may not be measured duringoperation of the energy system 10, and may be used as a parameter, whichcorrelates to, but is not that same as, energy variable inputs andoutputs u_(k), y_(k) for the boiler component block 50. Therefore, thisparameter may be calculated during operation of the energy system 10(and, more specifically, the components of the boiler component block50) with the parametric hybrid models 58, and may be used in calculatingan optimal trajectory for an input to the boiler (e.g. the firing rateof the boiler). This allows better real-time control during operation ofthe energy system 10, such that intermediate performance of the energysystem 10 may be more closely targeted and maintained. In certainembodiments, an optimal trajectory function may be determined bysolving:min(u _(k))Γ(ŷ _(k) ,ŷ _(k) ^(Trajectory)), subject to:w _(k) =f(u _(k),ρ);θ_(k) =f(u _(k) ,w _(k));x _(k) =F _(k)(u _(k) ,x _(k-1),θ_(k));y _(k) =G _(k)(u _(k) ,x _(k),θ_(k)); andL<u _(k) <H;

where Γ( ) is the objective function defined over energy variableoutputs, ŷ_(k) is the energy variable outputs (ŷ∈y), and ŷ_(k)^(Trajectory) is an explicit or implicit representation of a desiredenergy variable trajectory. In addition, constraints (e.g., L and Habove) may be trajectory functions. The minimization of the aboveobjective function is achieved through adjustments to the decisionvariables u_(k) (e.g., the energy variable inputs). Note that theoptimization problem above is merely exemplary and not intended to belimiting. For example, the objective function Γ( ) may be defined toinclude penalties on decision variables u_(k).

The dynamic optimization described above may be implemented usingvarious methods. The level of detail included in the parametric hybridmodels 58 may vary depending upon the level of complexity that may behandled in real time. In other words, the parametric hybrid modelingallows a systematic way of compromising between model accuracy andcomputational complexity and, therefore, offers flexibility to handleenergy systems 10 of varying levels of complexity. More specifically,the complexity of any given parametric hybrid model 58 is a function ofboth the complexity of the system being modeled, and the simplicity ofthe parametric hybrid model 58 needed to make real-time computationstractable. As such, the parametric hybrid model framework offers asystematic framework for optimally trading off model accuracy versuscomputational efficiency. In defining parametric hybrid models 58, incertain embodiments, short-cut models may be used (e.g., in theparametric first-principles models 64). These short-cut models may belinear or nonlinear, dynamic or steady-state, and so forth. Theparametric hybrid model framework remains current with the real-timeoperating conditions of the energy system 10, and allows for onlinemodification of the model parameters, which are not direct inputs oroutputs of the energy system 10, and hence the decision engine (i.e.,the optimization and control) always has valid models upon which to basedecisions.

The parametric hybrid model 58 models both steady-state and thenon-steady-state behavior of the processes of the energy system 10,whether the behavior is linear or nonlinear, with respect to criticalvariables, where gains and/or dynamics vary during operation of theenergy system 10. The optimization problem formulation for optimizationand/or control of the energy system 10 has: (1) parametric hybrid models58 of the components of the energy system 10, (2) parametric hybridmodels 58 of how these components are connected together to define theenergy system 10, (3) a parametric hybrid description of what theperformance objectives are, and (4) a parametric hybrid description ofwhat the constraints are. It should be noted that a parametric hybridmodel/description may degenerate to a constant in simple cases. Some ofthe variables (e.g., the parameters described herein) that areindicative of performance of the energy system 10 (or individualcomponents of the energy system 10) may not be measured or even easilymeasurable. The parametric hybrid models 58 are used to model thesevariables (e.g., the parameters described herein) as well. Then, anoptimizer may make decisions as to which inputs to the energy system 10should be given system models/objectives/constraints. As such, theparametric hybrid model framework allows all of the models to remaincurrent, while solving the optimization problem (i.e., making decisions)as quickly as possible. Achieving these two goals enables the optimalenergy management system to continuously make the best decisions basedon what is actually happening with the energy system 10 in substantiallyreal-time during operation of the energy system 10.

As described above with respect to FIG. 2, each component block 50, 52,54, 56 may be associated with energy loops 40, 42, 44, 46, 48 thatcontribute to operation of the component block 50, 52, 54, 56. Inaddition, each component block 50, 52, 54, 56 will include actualenergy-related equipment components. Moreover, each component block 50,52, 54, 56 may be modeled by a parametric hybrid model 58 as describedabove with respect to FIG. 3. For example, FIG. 4 is a block diagram ofan exemplary evaporation chiller block 54 of FIG. 2. As illustrated, theevaporation chiller block 54 may include a condenser 66, a compressor68, an evaporator 70, and a valve 72. As such, the evaporation chillerblock 54 may be associated with the condenser loop 44 (e.g., thecondenser 66), the electric loop 42 (e.g., the compressor 68), and theevaporator loop 46 (e.g., the evaporator 70).

Accordingly, the variables of the condenser loop 44, the electric loop42, and the evaporator loop 46 will be associated with the evaporationchiller block 54. More specifically, the variables ts^(C), tf^(C),f^(C), kw, ts^(E), tf^(E), and f^(E) comprise input and output energyvariables u_(k), y_(k) for the evaporation chiller block 54. However, aparametric hybrid model 58 may be built that incorporates fundamentalmodels for the condenser 66, compressor 68, evaporator 70, and valve 72(e.g., in a parameter model 62), empirical data relating to thecondenser 66, compressor 68, evaporator 70, and valve 72 (e.g., in anempirical model 60), and a parametric first-principles model 64 for theevaporation chiller block 54. From this, the parametric hybrid model 58of the evaporation chiller block 54 will model critical parameters θ_(k)of the evaporation chiller block 54. These critical parameters θ_(k) aredifferent from the input and output energy variables u_(k), y_(k) forthe evaporation chiller block 54. However, they correlate withperformance criteria of the evaporation chiller block 54. For example,critical parameters of the evaporation chiller block 54 may includeentropy production and thermal resistance. These parameters correlatewell with, but are not equal to, the input and output energy variablesu_(k), y_(k) for the evaporation chiller block 54 (e.g., ts^(C), tf^(C),f^(C), kw, ts^(E), tf^(E), and f^(E)).

As another example, FIG. 5 is a block diagram of an exemplary boilerblock 50 of FIG. 2. As illustrated, the boiler block 50 may include afurnace 74, an economizer 76, and a steam drum 78. As such, the boilerblock 50 may be associated with the fuel loop 40 (e.g., the furnace 74and the economizer 76) and the steam loop 48 (e.g., the steam drum 78).Accordingly, the variables of the fuel loop 40 and the steam loop 48will be associated with the boiler block 50. More specifically, thevariables t^(G), p^(G), f^(G), r, t^(S), p^(S), and f^(S) comprise inputand output energy variables u_(k), y_(k) for the boiler block 50.However, a parametric hybrid model 58 may be built that incorporatesfundamental models for the furnace 74, economizer 76, and steam drum 78(e.g., in a parameter model 62), empirical data relating to the furnace74, economizer 76, and steam drum 78 (e.g., in an empirical model 60),and a parametric first-principles model 64 for the boiler block 50. Fromthis, the parametric hybrid model 58 of the boiler block 50 may generatemodels for critical parameters θ_(k) of the boiler block 50. Thesecritical parameters θ_(k) are different from the input and output energyvariables u_(k), y_(k) for the boiler block 50. However, they correlatewith performance criteria of the boiler block 54. For example, criticalparameters of the boiler block 50 may include the efficiency of thefurnace. This parameter correlates well with, but is not equal to, theinput and output energy variables u_(k), y_(k) for the boiler block 50(e.g., t^(G), p^(G), f^(G), r, t^(S), p^(S), and f^(S)).

Therefore, parametric hybrid models 58 can be built for variouscomponent blocks 50, 52, 54, 56 of the energy system 10. Components ofthe component blocks 50, 52, 54, 56 may include power generation units,such as gas turbines, wind turbines, solar panels, and so forth. Asdescribed above, an electricity grid 18 may also be considered as apower generation source, and may be modeled using the parametric hybridmodels 58. Other components of the component blocks 50, 52, 54, 56 thatmay be modeled include chillers (e.g., such as illustrated in FIG. 4),boilers (e.g., such as illustrated in FIG. 5), cooling towers, pumps,fans, motors, thermal storage units, and so forth. In addition,parametric hybrid models 58 may be developed for loads, such as steamloads, chilled water loads, electricity loads, and so forth.Furthermore, other parametric hybrid models 58 may be developed forvarious power generation sources and power consumption components. Inaddition, not only may parametric hybrid models 58 be developed forcomponent blocks 50, 52, 54, 56, such as those illustrated in FIG. 2,but parametric hybrid models 58 of the interconnections (e.g., theenergy loops 40, 42, 44, 46, 48) between the components may also bedeveloped.

The parametric hybrid models 58 will capture the performance andeconomics of the operation of the energy system 10, operationalconstraints of the energy system 10, existing knowledge regardingoperation of the energy system 10, and objectives for the operation ofthe energy system 10. The optimal operating conditions of the energysystem 10 may be determined via a systematic optimization problem usingan appropriate solver (e.g., an algorithmic search for the bestsolution). However, in other embodiments, the optimal operatingconditions of the energy system 10 may be determined using heuristicsearches, rule-based reasoning, fuzzy logic, and so forth. Anotheraspect of the disclosed embodiments is the ability to modify theparameters of the parametric hybrid models 58 defining the energy system10 based on updated data regarding new operating conditions of theenergy system 10.

Various embodiments of systems and methods for applying parametrichybrid models 58 are described below. In this approach, the parametrichybrid models 58 that define the energy system 10 may be incorporated asan integrated model in a parametric hybrid model-based systemmanager/controller. This system may project or predict what will happenin the energy system 10 based on the integrated parametric hybrid model58 and recent historical data including, for example, recent operatingconditions and/or state values, and predictions of weather/load that maybe obtained from many resources, including other parametric hybridmodels 58, among other things. This projection or prediction may beupdated or biased based on received current information, specifiedobjectives, and/or constraints of the energy system 10. Optimizationalgorithms may be used to estimate the best current and future controladjustments on the model inputs to achieve a desired response of theenergy system 10. Targets are set and the integrated parametric hybridmodel outputs may be compared to how that output behaves in order tomaintain the desired accuracy of the integrated parametric hybrid models58.

As described above, parametric hybrid models 58 may be developed for anyof the component blocks of a system (e.g., the component blocks 50, 52,54, 56 of the energy system 10 described above). In addition, theparametric hybrid models 58 may be linked together to form networks ofparametric hybrid models 58 that interact with each other in aplant-wide or enterprise-wide manner. As such, not only do theindividual parametric hybrid models 58 model complex operation forindividual component blocks of the system 10, but the interactionsbetween the individual parametric hybrid models 58 form networks havingcomplex data flows and constraints between the parametric hybrid models58.

A graphical modeling tool may be used to define relationships and dataflows between parametric hybrid models 58. More specifically, thegraphical modeling tool may be configured to represent relationshipsbetween components of a system (e.g., spatial relationships between thecomponents, fluid flows between the components, product flows betweenthe components, power flows between the components, and so forth),wherein the components that are represented by the graphical modelingtool are modeled using the parametric hybrid models 58. For example,FIG. 6 is an example of a graphical user interface 80 (i.e., a graphicalrepresentation) of the graphical modeling tool 82 representing aplurality of parametric hybrid models 58 relating to components of thesystem 10 arranged as a network 84. In particular, in the illustratedexample, the system 10 includes a power grid component block 86 (i.e.,P.0), which functions as a power source for four chiller componentblocks 88 (i.e., EC.0, EC.1, EC.2, and EC.3), and a chilled watercomponent block 90 (i.e., CW.0), which functions as a sink for the fourchiller component blocks 88. Each of the component blocks 86, 88, 90 ismodeled as a parametric hybrid model 58 as described above, and isgraphically represented as a node 92 that may be connected to the othernodes 92 (i.e., the other component blocks 86, 88, 90) via connections94, which is also modeled as a parametric hybrid model.

Each of the nodes 92 relating to the component blocks 86, 88, 90 andconnections 94 for the component blocks 86, 88, 90 are defined such thatthe exemplary network 84 in FIG. 6 unambiguously defines a well-posedoptimization problem. As such, each of the nodes 92 and connections 94are characterized by decision variables and parameters in theoptimization problem. Therefore, in the graphical representation of theoptimization problem, the nodes 92 capture how decision variablesinfluence the objective functions. This distinguishes the graphicalrepresentation of the optimization problem (exemplified in network 84)from the graphical representations commonly used to simulate a process,as the connections between nodes in a simulation scenario reflect thephysical impact of one node's output as input to another node. Thesemore common input and output flows to and from the nodes 92 in thenetwork 84 (such as the ones needed for simulating a process) arecompletely abstracted from the decision variables. Therefore, each ofthe connections 94 includes a direct translation into the optimizationproblem that is constructed and maintained by the graphical language.This allows the parametric hybrids models 58 and the connections 94between the parametric hybrid models 58 to be developed by modelingexperts, but the graphical components illustrated in FIG. 6 to beviewable by any users of the system 10 that have access to the graphicalmodeling tool 82 and are authorized to view and/or modify the parametrichybrid models 58 relating to the graphical components.

FIG. 7 is a block diagram of an enterprise-integrated parametric hybridmodel enabled control/optimization system 96 for controlling andoptimizing the system 10 of FIG. 1. As described in greater detailbelow, the control/optimization system 96 includes the graphicalmodeling tool 82, which enables the graphical user interface 80illustrated in FIG. 6 to be displayed to users of thecontrol/optimization system 96. More specifically, users who have accessto the control/optimization system 96 may display the graphical userinterface 80 on any compatible electronic devices to interact withparametric hybrid models 58 representing components of the system 10. Asillustrated in FIG. 7, the control/optimization system 96 is directlyconnected to the system 10. More specifically, in certain embodiments,the control/optimization system 96 may include a plurality of sensors 98and actuators 100 that are connected to individual components 102 (i.e.,physical equipment) of the system 10. Generally speaking, the sensors 98are configured to receive signals relating to operating information ofthe components 102 of the system 10, and the actuators 100 areconfigured to receive signals transmitted by the control/optimizationsystem 96 for controlling operation (i.e., valve settings, pump andcompressor speeds, and so forth) of the components 102.

As such, the control/optimization system 96 is a computer system forcontrolling operation of the system 10. The control/optimization system96 may include any of various types of computer systems or networks ofcomputer systems, which execute software programs 104 according tovarious embodiments described herein. The software programs 104 mayperform various aspects of modeling, prediction, optimization, and/orcontrol of the system 10. The control/optimization system 96 may furtherprovide an environment for making optimal decisions using anoptimization solver and carrying out those decisions (e.g., to controlthe system 10). In particular, the control/optimization system 96 mayimplement parametric hybrid model control of the system 10. Morespecifically, the parametric hybrid models 58 relating to the components102 of the system 10 may be utilized to enable the parametric hybridmodel control of the system 10.

In addition, the control/optimization system 96 is configured togenerate and transmit the graphical user interface 80 depicted in FIG. 6to remote users 106 of the control/optimization system 96. Morespecifically, the control/optimization system 96 is configured totransmit graphical user interfaces 80 across a communication network 108to electronic devices 110 that may be located remotely from the system10. For example, in certain embodiments, the communication network 108may include a local area network (LAN). However, the communicationnetwork 108 may also include the Internet, with the control/optimizationsystem 96 functioning as a server to generate and transmit the graphicaluser interfaces 80 to electronic devices 110 located anywhere. Theelectronic devices 110 may be desktop computers, laptops computers,smart phones, or any other electronic devices capable of displaying thegraphical user interfaces 80 on a display 112 of the electronic device110, and capable of receiving inputs from the user 106 of the electronicdevice 110 via interfaces 114 of the electronic device 110. Thecontrol/optimization system 96 is designed such that potentiallyasynchronous inputs from local or remote users 106 are alwaysincorporated into the online model after proper integrity checks by theparametric hybrid models 58. These integrity checks are embedded withinthe parametric hybrid models 58 when these models are defined.

The control/optimization system 96 includes a non-transitory memorymedium 116 on which the software programs 104, data relating to theparametric hybrid models 58, operating data (both real-time andhistorical) for the system 10, and so forth, are stored. The term“memory medium” is intended to include various types of memory orstorage, including an installation medium (e.g., a CD-ROM, or floppydisks), a computer system memory or random access memory such as DRAM,SRAM, EDO RAM, Rambus RAM, and so forth, or a non-volatile memory suchas a magnetic medium (e.g., a hard drive), or optical storage. Thememory medium 116 may comprise other types of memory as well, orcombinations thereof. A processor 118 executing code and data from thememory medium 116 comprises a means for creating and executing thesoftware programs 104 according to the methods described herein. Thecontrol/optimization system 96 may take various forms, including apersonal computer system, mainframe computer system, workstation,network appliance, Internet appliance, or other device. In general, theterm “computer system” can be broadly defined to encompass any device(or collection of devices) having the processor 118 (or processors),which executes instructions from the memory medium 116 (or memorymedia).

The users 106 of the control/optimization system 96 may have varyingsecurity access levels, which may be determined when the users 106 enterlogin credentials into the electronic devices 110, or may be determinedusing other methods, such as having access rights stored on theelectronic devices 110, and so forth. For example, as illustrated inFIG. 7, the users 106 of the control/optimization system 96 may includemanager-level users 120 and engineer-level users 122 (e.g., plantengineers or operators). As described in greater detail below, themanager-level users 120 may have access to only a subset of the features(e.g., command inputs) available to the engineer-level users 122. Forexample, the manager-level users 120 may be allowed to modifyoptimization constraints of the parametric hybrid models 58 representingthe components 102 of the system 10, whereas the engineer-level users122 may be allowed to modify optimization constraints of the parametrichybrid models 58 as well as also modifying the underlying parametrichybrid models 58. As such, the command inputs that are enabled in thegraphical user interfaces 80 transmitted to the users 106 will varydepending on the security access levels of the particular users 106.

In certain embodiments, when a user 106 submits a command input (e.g.,clicking on a node 92 or connection 94 to interact with the node 92 orconnection 94), other users 106 of the control/optimization system 96will be notified of the command input in substantially real-time (e.g.,during operation of the system 10). In other words, the command inputwill be transmitted from the electronic device 110 being used by theuser 106 to the control/optimization system 96, and the effect of theprocessed command input will be pushed out (i.e., broadcast) to otherelectronic devices 110 being used by other users 106. As such, theinteractions that occur with the parametric hybrid models 58 will betransparent to all users 106 of the control/optimization system 96. Theusers 106 may also interact with the control/optimization system 96 in asand-box mode where all the changes are understood to be local to theparticular user 106 and have no impact on the online application. Thissand-box mode allows each user 106 to perform what-if analysis, forexample, using the most current state of the system 10 withoutinterfering with the online application. While the simulated what-ifscenarios may be recorded locally (e.g., on the electronic device 110),in certain embodiments, any commitment of changes to thecontrol/optimization system 96 may be subject to an authorizationprocess. For example, an engineer-level user 122 may have to approve thewhat-if scenarios before they are committed.

Furthermore, each model is deployed as a server service that can servemultiple requests to multiple electronic devices 110. This enables allusers 106 to investigate the functioning of the parametric hybrid models58 during operation of the system 10. More specifically, as the model isdeployed and running, each node 92 (e.g., the component blocks relatingto components 102 of the system 10) is capable of providing informationto the users 106 via the graphical user interfaces 80. As such, theusers 106 are able to view data relating to accuracy of the modelsduring operation of the system 10. In addition, the same deployed modelwill be capable of providing other services, such as being used forcalculating key performance indicators at the same time that it is beingutilized by the control/optimization system 96.

As described above, model validation has conventionally been viewed asan offline activity. However, the embodiments described herein embed thelogic for data filtering and the algorithms for parameter identification(e.g., as a closed-form solution) and optimization as properties of thedeployed parametric hybrid models 58 and create the model qualitymeasure as a parameter of the parametric hybrid models 58. Morespecifically, again, the graphical modeling tool 82 functions as aserver service, allowing the deployed online model (i.e., the network 84of parametric hybrid models 58) to avoid performance degradation whenthe model quality measure is calculated. In certain embodiments, themodel quality is mapped to model parameters, such that model qualityinformation is made available to the users 106 of thecontrol/optimization system 96. For example, using the parametric hybridmodels 58, model error may be easily associated with model parameters(e.g., by defining acceptable ranges for the parameters), and the users106 may take specific actions in response to model qualitydeterioration.

The deployment strategy for the transparent parametric hybrid models 58enables distributed and asynchronous validation and modification of thedeployed model. This is particularly advantageous inasmuch as thecomponents of the model are distributed throughout the plant and/orenterprise. In addition, the transparency is two-way. In other words,while model quality is accessible to any authorized user 106 of thecontrol/optimization system 96, any modification by any authorized user106 is transparent to all authorized users 106. Furthermore, theparametric nature of the model enables graphical representation of themodel quality (e.g., bounds on model parameters, where the current valueof the parameter falls within the bounds, and so forth).

Because the transparent parametric hybrid models 58 are composed ofpotentially distributed components 102 with corresponding owners andstakeholders of the components 102, the integrity of the deployed modelis ensured through efficient ownership modeling. For example, modelownership (e.g., of specific parametric hybrid models 58, and so forth)is an intrinsic property of the deployed model. The ownership propertyfor specific parametric hybrid models 58 is used as a key by whichaccess and modification of the parametric hybrid models 58 may beauthenticated and implemented. In other words, if the user 106 is not anowner of a particular parametric hybrid model 58, or does not havesufficient access rights to the parametric hybrid model 58, the user 106may be prevented from interacting with the parametric hybrid model 58.In other words, the graphical user interface 80 presented to the user106 via the electronic device 110 only presents the user 106 withactions (i.e., command inputs) to which the user 106 has access. Theownership property applies to both nodes 92 and connections 94 of themodel network 84 for the plant and/or enterprise and, therefore, theownership properties are used for validation of any graphicalmanipulation of the parametric hybrid models 58 (e.g., addition anddeletion of parametric hybrid models 58 to and from the model network84).

In addition, certain graphical manipulations (i.e., command inputs) ofthe parametric hybrid models 58 performed by certain users 106 may besubject to approval by other users 106 before being implemented. Forexample, in certain embodiments, command inputs performed bymanager-level users 120 may be subject to approval by engineer-levelusers 122 before being implemented. This approval mechanism is enabledby the transparent nature of the graphical modeling tool 82 inasmuch ascommand inputs performed by any users 106 of the control/optimizationsystem 96 are pushed to the graphical user interfaces 80 of otherdevices 110 connected to the control/optimization system 96 insubstantially real-time.

For example, returning now to FIG. 6, the users 106 of the graphicalmodeling tool 82 need only interact with the graphical information viathe graphical user interface 80. For example, if the user 106 wishes toadd or modify a constraint of the system 10, the user 106 need onlyclick on a node 92 or connection 94, which brings up a dialog box thatenables the user 106 to add the constraint information. In addition, theusers 106 of the graphical modeling tool 82 may add and/or deletecomponent blocks from the graphical user interface 80. In other words,the component blocks represented in any given network 84 via thegraphical user interface 80 need not represent all of the physicalcomponents 102 of the actual system 10 that is being modeled andoptimized. Rather, the user 106 may only be interested in (or haveaccess to) certain sets of the physical components 102 of the system 10.As such, the user 106 may personalize the graphical user interface 80 toinclude component blocks of interest to the user 106.

For example, FIG. 8 is an example of the graphical user interface 80(i.e., a graphical representation) of the graphical modeling tool 82illustrating a library 124 of component blocks available to the user 106to be added to the graphical user interface 80. For example, the user106 may drag-and-drop any of the component blocks listed in the library124 into the graphical user interface 80. In certain embodiments, thegraphical modeling tool 82 will automatically create and/or remove theappropriate connections 94 between component blocks (i.e., the nodes 92)that are added and/or deleted by the user 106 via the graphical userinterface 80 being viewed by the user 106. In addition, it will beunderstood that the settings of the personalized graphical userinterfaces 80 created by the users 106 may be saved and re-opened asneeded.

As such, any particular graphical representation of the system 10 mayconvey different information to the user 106 depending on the context inwhich the graphical representation is involved. For example, if a Modeltab 126 of the graphical modeling tool 82 is selected by the user 106,and a connection 94 between one of the four chiller component blocks 88(i.e., EC.0, EC.1, EC.2, and EC.3) and the chilled water component block90 is clicked, a dialog box may be initiated, displaying the flow rate,temperature, and pressure of the chilled water leaving the chillercomponent block 88, for example. However, if an Network tab 128 of thegraphical modeling tool 82 is selected by the user (assuming the userhas access to the Network tab 128), and the connection 94 between thechiller component block 88 and the chilled water component block 90 isclicked, a dialog box may be initiated, displaying the chilled watertonnage produced by the chiller component block 88, for example.

In other words, the decision variables or constraints (e.g., parameters)of the parametric hybrid models 58 representing the component blocks areaccessible to users 106 when the Network tab 128 is selected (i.e., whenin Network mode). However, the actual physical inputs and outputs thatdescribe the particular equipment are not displayed when the Network tab128 is selected (i.e., when in Network mode). Rather, the actualphysical inputs and outputs that describe the particular equipment areonly displayed to the user when the Model tab 126 is selected (i.e.,when in Modeling or Operation mode). As such, in certain embodiments,only the users 106 (e.g., the engineer-level users 122) having thein-depth knowledge of the parametric hybrid models 58 representing thecomponent blocks may have access to the Model tab 126. Therefore, onlythese users 106 will be capable of interacting with the actual physicalinputs and outputs of the particular equipment. Conversely, any users106 of the system 10 that have access to the Network tab 128 may becapable of interacting with the decision variables of constraints of thesystem 10 for the purpose of performing optimization and control of thesystem 10.

Each node 92 in a network 84 can represent an objective function foroptimization and control of the system 10. This can be particularlybeneficial if multiple operational objectives are to be handledgraphically via the graphical user interface 80. Various objectives maybe capable of being interacted with via the graphical user interface 80and, as such, the user 106 may graphically modify the optimizationproblem for the system 10. For example, in certain embodiments, thegraphical modeling tool 82 may present the user 106 with a range ofvalues within which an optimization constraint for a particularparametric hybrid model 58 may be modified. In other words, withoutrequiring approval by engineer-level users 122, the graphical userinterface 80 may allow a manager-level user 120 to modify anoptimization constraint within a bounded range of feasible values forcontrol of the system 10.

Any and all command inputs submitted by the users 106 may redefine theoptimization objectives for the system 10. For example, a chillernetwork (e.g., the network 84 illustrated in FIGS. 6 and 8) receivingelectric energy and producing chilled water may be optimized to producea chilled water load with minimal energy use, or to maximize the chilledwater production given a maximum available electric energy, throughcommand inputs submitted via the graphical user interface 80 by the user106. For example, when an Optimization tab 130 is selected, the user 106may interact with optimization constraints of the network 84.

For example, FIG. 9 is an example of the graphical user interface 80(i.e., a graphical representation) of the graphical modeling tool 82illustrating an optimization view 132 when the Optimization tab 130 isselected by the user 106. More specifically, with the Optimization tab130 selected, FIG. 9 illustrates when the user 106 clicks the chilledwater component block 90. As such, the optimization view 132 depicted inFIG. 9 illustrates a time series 134 of the projected chilled waterdemand of the chilled water component block 90. In addition, theoptimization view 132 for the chilled water component block 90 includestime schedules 136 for each of the four chiller component blocks 88 thatare connected to the chilled water component block 90. Morespecifically, the time schedules depict when each of the chillercomponent blocks 88 are scheduled to be operative to achieve theprojected chilled water demand of the chilled water component block 90.

Assuming the user 106 is authorized to interact with the chilled watercomponent block 90, the user 106 may modify an optimization constraintof the chilled water component block 90 via the optimization view 132 ofthe graphical user interface 80. For example, FIG. 10 is an example ofthe graphical user interface 80 (i.e., a graphical representation) ofthe graphical modeling tool 82 illustrating the optimization view 132when the user 106 has submitted a command input (i.e., modified anoptimization constraint) and the optimization solution of the system 10has been updated. More specifically, in the example depicted in FIG. 10,the user 106 has modified the time series 134 of the projected chilledwater demand of the chilled water component block 90, and the timeschedules 136 of the four chiller component blocks 88 have been updated.In particular, the model of the control/optimization system 96 hasupdated the optimization problem of the system 10 to determine thatchiller component block EC.0 should be turned off between 16:00 and18:00 and that chiller component block EC.2 should be turned on between16:00 and 18:00. As illustrated in FIG. 10, a cost of the committedmodification is presented to the user 106 (e.g., at the bottom of thegraphical user interface 80). In certain embodiments, the cost ofintroducing the optimization constraints may be reported to all users106, and recorded in appropriate formats (e.g., in a database residingwithin the control/optimization system 96, for example). This type ofmodification of optimization constraints may be performed for any of thecomponent blocks (i.e., parametric hybrid models 58) of the network 84displayed by the graphical user interface 80. Due to the globaloptimization strategy in the control/optimization system 96, the cost ofrespecting newly defined constraints by the user 106 is calculated andshown immediately to the user as shown in FIG. 10. The ability tographically vary the load profile (e.g., time series 134) andimmediately see the costs/savings under various load profiles is aunique capability enabled by the graphical language for optimizationpresented herein.

The components blocks are parametric hybrid models 58 and, as such, arenot linear models in general (even though linear models are degenerateforms of parametric hybrid models 58). Therefore, the networks 84comprised of the parametric hybrid models 58 are similarly not going tobe linear optimization problems. Accordingly, when a user 106 modifiesan optimization constraint of a parametric hybrid model 58, thedetermination of a well-posed modified optimization problem is somewhatcomplex. A preferred method for determining the modified optimizationproblem for the graphical optimization language is to use a data-drivenconvex approximation over a trajectory for each parametric hybrid model58 in the network model 84. By definition, a function ƒ is convex if:ƒ(λx+(1−λ)y)≤λƒ(x)+(1−λ)ƒ(y),∀x,y∈D _(ƒ),∀λ∈[0,1]

Furthermore, if ƒ and g are convex functions, then so is:αƒ+βg,∀α,β≥0

As a result, the overall model representing the network model 84 will beconvex. Any local minimum of a convex function is also a global minimum.Non-convex optimization problems benefit from tight, convexunderestimators. Assuming that ƒ is a twice differentiable function,then ƒ is convex if and only if:∇²ƒ(x)

0,∀x∈D _(ƒ)

In the graphical representation of the optimization problem (e.g. thenetwork model 84 shown in FIG. 8), each node 92 exposes decisionvariables for the optimization problem. Each connection 94 determineshow decision variables in two nodes 92 are related (e.g., constrained).Therefore, the graphical presentation has a direct translation into theoptimization problem statement. Network topology, and any modificationto the network topology via graphical interactions with the network 84(e.g. adding a node 92, removing a connection 94), can be captured bylinear matrix operations. Therefore, a graphical representation of theoptimization problem will translate into a well-posed optimizationproblem if each component in the network 84 is approximated with aconvex function. A preferred method for this convexification in thegraphical language disclosed herein is to use automatic data-drivenconvex approximation of the network components along a predictedoperation trajectory. The parametric hybrid modeling paradigm allows forthis convex approximation with desired degrees of accuracy. Therefore,the optimization problem for the model of the system 10 may be solvedusing convex approximation where successive convexification of feasibleregions may be performed, with iteration confined to the feasibleregions. For example, FIG. 11 is an example of a non-linear andnon-convex function 138 of two variables 140, 142 relative to eachother. As illustrated, two convex approximations 144, 146 provide convexunderapproximators with different accuracies.

In addition, in certain embodiments, the solution to the optimizationproblem 138 is not ascertained in a deterministic manner. In otherwords, the optimization solution is not determined independent of thepoint at which the determination is begun. Rather, the optimizationsolution may be determined with the previous optimization solution inmind. For example, returning to the example of the modification of theoptimization constraint described with respect to FIG. 10, the updatingof the optimization solution between 16:00 and 18:00 begins under theassumption that operating chiller component block EC.0, chillercomponent block EC.1, and chiller component block EC.3 during this timeperiod is the optimal solution. As such, the modified optimizationsolution merely changes the scheduling times 136 such that chillercomponent block EC.2 instead of chiller component block EC.0 is operatedduring this time period. In other words, the model attempts to reach anoptimization solution as close to the previous optimization solution aspossible (i.e., in a non-deterministic manner).

As an example, the scheduling problem formulation may be defined by thefollowing functions:

${{\min{\sum\limits_{i \in M}{\beta_{i}f_{i}}}} + {\sum\limits_{i \in M}{\sigma_{i}g_{i}}} + {\sum\limits_{j \in N}{\kappa_{j}r_{j}}}},{{such}\mspace{14mu}{that}}$p_(i) = Γ_(i)(A_(i)p, B_(i)r, φ_(i))  ∀i ∈ M f + Hp − g = 0 Zp ≥ δμ_(i)y_(i) ≤ p_(i) ≤ ξ_(i)y_(i)  ∀i ∈ M y_(i) ∈ {0, 1}  ∀i ∈ M

where M is the set of unit operations, N is the set of inputs, β, σ, andκ are costs associated with import of a product, sale of a product, andpurchase of a resource, respectively, r is a given resource input, p isproduct generated by a specific unit operation, A and B restrict unitoperation models, Γ, to a subset of products and inputs, φ is the set offitting parameters for a given model, μ and ξ are unit operation bounds,H, ƒ and g allow product import and export, Z and δ set demandrequirements, and y is a binary variable for unit status. The linearnetwork model constraints H and Z may be defined by the user 106 (e.g.,by clicking on a parametric hybrid model 58 via the graphical userinterface 80). In addition, the discrete (or binary) decision variablesy_(i) may also be defined by the user 106. Furthermore, the constraintparameters δ, ƒ, g, β, σ, and κ may also be defined by the user 106.

FIG. 12 is an example of a solution graph 148 for the optimizationsolution equations described above. The solution graph 148 may bereferred to as a directed tree D=(V, E), where V is the set of unitoperation models, products, and resources V=(Γ, p, r, f, g) and E is theset of connections E=(H, Z). In general, the set of unit operationmodels V is analogous to the parametric hybrid models 58 (i.e., thenodes 92 of the model network 84) and E is analogous to the connections94 of the model network 84. FIG. 12 clearly demonstrates that withnonlinear unit operation models, Γ, the well-posedness of theoptimization problem is not trivial, and graphical manipulation of thesolution graph is not easily manageable. Successive data-drivenconvexification is the preferred approach to render such solution graphgraphically manageable.

FIG. 13 is an example of a method 150 for utilizing the graphical userinterface 80 to interact with the parametric hybrid models 58 describedherein. In step 152, an access level of a user 106 may be determinedwhen the user 106 enters login credentials into a remote electronicdevice 110, or may be determined using other methods, such as havingaccess rights stored on the electronic device 110, and so forth. Forexample, as described above, when the user 106 logs into the electronicdevice 110, the graphical modeling tool 82 may determine that the user106 is a manager-level user 120 or an engineer-level user 122. However,other access levels may be used, which may enable a more granular levelof authorization and functionality.

In step 154, the graphical user interface 80 is made available from thegraphical modeling tool 82 of the control/optimization system 96 to theelectronic device 110. The graphical user interface 80 enables aplurality of command inputs relating to the parametric hybrid models 58(i.e., which relate to actual physical components of a plant and/orenterprise) of a model network 84, and corresponding to the access levelof the user 106. For example, assuming the user 106 has appropriateaccess rights to a particular parametric hybrid model 58, a commandinput for modifying an optimization constraint (e.g., a predicted loadprofile) for the parametric hybrid model 58 may be enabled via thegraphical user interface 80. In addition, again assuming the user 106has appropriate access rights to the particular parametric hybrid model58, a command input for modifying how the parametric hybrid model 58functions (e.g., the inputs, outputs, parameters, and so forth, of theparametric hybrid model 58) may be enabled via the graphical userinterface 80.

Furthermore, as described in greater detail above, the graphical userinterface 80 enables the display of a plurality of parametric hybridmodels 58 represented as nodes 92 of a model network 84, and a pluralityof inputs and outputs of the plurality of parametric hybrid models 58represented as connections 94 between the nodes 92 of the model network84. The graphical user interface 80 enables the user 106 to add ordelete nodes 92 and connections 94 from the model network 84 to create apersonalized display of the parametric hybrid models 58 with which theuser 106 is authorized to interact.

In step 156, a command input is received from the graphical userinterface 80 by the graphical modeling tool 82 of thecontrol/optimization system 96. As described above, in certainembodiments, the command input may be transmitted (i.e., broadcast) toother users 106 of the control/optimization system 96 via otherelectronic devices 110. Then, in step 158, the command input isprocessed by the graphical modeling tool 82 according to the accesslevel of the user 106 submitting the command input. For example, incertain embodiments, a model quality of one or more of the parametrichybrid models 58 may be determined during operation of the system 10. Asdescribed above, the ability to interrogate model quality duringoperation of the system 10 is due to the transparent nature of thegraphical modeling tool 82. In addition, in certain embodiments, theoptimization problem of the model network 84 may be automaticallyre-adjusted by the control/optimization system 96 during operation ofthe system 10, assuming the user 106 has authorization to make such arequest, and that the request is feasible. However, in certainembodiments, the command input may also be subject to approval by anengineer-level user 122, subject to bounding constraints (e.g., onlychanges within specific ranges may be allowed), and so forth, prior toexecution by the control/optimization system 96.

Regardless, the command inputs may all be used to modify control of thesystem 10 during operation of the system 10 via the control/optimizationsystem 96. For example, using the example described above with respectto FIG. 10, if a user modifies an optimization constraint of one of theparametric hybrid models 58, and the modification is found to befeasible by the control/optimization system 96 (i.e., via the graphicalmodeling tool 82), then the resulting optimization solution may beautomatically implemented by the control/optimization system 96. Forexample, actuators 100 of the components 102 of the system 10 may beactuated in accordance to the revised optimization solution. Again,using the example described with respect to FIG. 10, thecontrol/optimization system 96 may automatically control the system 10to shut down chiller component block EC.0 between 16:00 and 18:00, andto start up chiller component block EC.2 between 16:00 and 18:00.

Returning now to FIG. 8, in certain embodiments, the nodes 92 of themodel network 84 may be selected via the graphical user interface 80and, upon selection, the graphical representation of the model network84 that is depicted by the graphical user interface 80 may bere-organized (e.g., rearranged) based on the selection. For example,FIG. 14 is an example of the graphical user interface 80 (i.e., agraphical representation) of the graphical modeling tool 82 illustratinga particular model network 84 being illustrated in the network modelpane 160 of the graphical user interface 80. In the example illustratedin FIG. 14, the network model 84 includes 17 nodes 92 in total. Morespecifically, as illustrated in the model pane 162 of the graphical userinterface 80, the model network 84 includes a “FUEL GAS” node 164, a“CAMPUS ELECTRIC” node 166, seven nodes 92 associated with a “WESTPLANT” (i.e., an “ELECTRIC CHILLERS 0” node 168, an “ELECTRIC CHILLERS1” node 170, an “ELECTRIC CHILLERS 2” node 172, a “TURBINE CHILLERS 0”node 174, a “CW.WEST” (chilled water) node 176, an “LP STEAM” node 178,and an “ABSORPTION CHILLERS 0” node 180), and eight nodes 92 associatedwith a “POWER PLANT” (i.e., an “HP STEAM” node 182, a “BOILER 1” node184, a “TurbineGen” node 186, a “GENERATED POWER” node 188, a“CONDENSATE WATER” node 190, an “HHP STEAM” node 192, a “PRV342” node194, and a “BOILER 2” node 196).

Any one of these nodes 92 of the model network 84 may be selected by theuser in order to graphically rearrange the location and orientation ofthe graphical positioning of the nodes 92 and connections 94 of themodel network 84 and/or change an associated optimization order of thenodes 92 of the model network 84. For example, FIG. 14 illustrates thedepicted model network as being optimized for electricity usage of themodel network 84. As such, the “CAMPUS ELECTRIC” node 166 is illustratedat the top of the graphical representation of the model network 84. Notonly is this node 166 graphically represented as being at the “top” ofthe model network 84, the selection of this node 166 adjusts theunderlying optimization prioritizations of the model network 84. Morespecifically, in certain embodiments, selecting the “CAMPUS ELECTRIC”node 166 in this manner sets the electricity usage of this node to bethe top priority for the model network 84. It will be understood that,in certain embodiments, energy usage associated with the particularselected node 92 may be optimized upon selection of the node 92 and, inother embodiments, energy usage of all nodes 92 associated with theenergy type (e.g., electricity usage in this example) of the selectednode 92 may be optimized upon selection of the node 92.

Selecting another node 92 of the model network 84 may cause thegraphical representation of the model network 84 to change, as well asre-prioritize the newly selected node 92. For example, FIG. 15 is anexample of the graphical user interface 80 (i.e., a graphicalrepresentation) of the graphical modeling tool 82 illustrating the modelnetwork 84 of FIG. 14, wherein the “FUEL GAS” node 164 has been selectedas the prioritized node. As illustrated in FIG. 15, the positioning ofthe nodes 92 of the model network 84 within the network model pane 160of the graphical user interface 80 has been changed in response to theselection of the “FUEL GAS” node 164. In addition, the positioning ofall of the nodes 92 and the associated connections 94 will transitionfrom what is illustrated in FIG. 14 to what is illustrated in FIG. 15 inreal-time with an associated animation of the movements of each of thenodes 92 and connections 94 from their graphical positioning in FIG. 14to their graphical positioning in FIG. 15. For example, the graphicalposition of the “CONDENSATE WATER” node 190 from FIG. 15 is illustratedin FIG. 14 in dashed lines. The transition trajectory 198 from thegraphical position of the “CONDENSATE WATER” node 190 illustrated inFIG. 14 to the graphical position of the “CONDENSATE WATER” node 190illustrated in FIG. 15 is illustrated in FIG. 14 as well. Again, as thegraphical positioning of the “CONDENSATE WATER” node 190 transitionsfrom these two positions, the movement of the “CONDENSATE WATER” node190 will be animated in real-time along the transition trajectory 198.As such, the “CONDENSATE WATER” node 190 will seemingly move along thetransition trajectory 198 from the graphical position of FIG. 14 to thegraphical position of FIG. 15.

In addition, all of the other nodes 92 and connections 94 will alsotransition in a similar manner. It will be understood that theconnections 94 will not simply translate from their graphical positionin FIG. 14 to their graphical position of FIG. 15. Rather, the endpoints (i.e., connected to the respective nodes 92) of each of therespective connections 94 will translate, and the graphical line thatrepresents the respective connection 94 will translate, rotate, andchange length, as necessary, such that the associated end points (i.e.,connected to the respective nodes 92) are graphically maintained.

As described above, not only will selection of a particular node 92 leadto graphical re-organization of all of the nodes 92 and connections 94of the model network 84, but the underlying optimization solutions maybe adjusted as well. For example, while electricity usage is optimizedin FIG. 14, fuel gas usage will become optimized once the “FUEL GAS”node 164 is selected (i.e., FIG. 15). In other words, the prioritizationof optimization will shift from electricity usage in FIG. 14 to fuel gasusage in FIG. 15. The selection of the nodes 92 that will initiate there-organization of the nodes 92 and the connections 94 as illustrated inFIGS. 14 and 15 will vary in certain embodiments. For example, incertain embodiments, the re-organization of nodes 92 and connections 94may be initiated by a user double-clicking a mouse button, clicking aright mouse button, and so forth, while a cursor is hovering over theparticular node 94 in the network model pane 160 of the graphical userinterface 80.

In addition to the user-based security of the parametric hybrid models58 described above, an overall framework for securing proprietary (e.g.,protected) information relating to the parametric hybrid models 58 mayalso be implemented. In particular, the embodiments described hereininclude a workable model-based optimization and control system withsecure models. The embodiments described herein overcome certainchallenges preventing the use of content-protected models in model-basedoptimization and control applications. For example, one challengeincludes the absence of a general theory for efficiently approximatingcomplex dynamic models. Without the ability to guarantee a desiredaccuracy level, conventional model-based optimization and controlsolutions typically rely on the most comprehensive representation of thesystem for numerical computations. In addition, online optimization isgenerally a relatively computationally expensive exercise. The additionof an online approximation capability only adds to the onlinecomputation cost. This has been perceived a formidable cost and, hence,real-time model approximation has not been previously attempted inoptimization solutions in which explicit optimization is carried out.

In addition, the need to use a model over a prediction horizon furthercomplicates real-time model approximation. Conventional model-basedoptimization and control solutions typically solve a constrainedoptimization problem over a horizon of interest (e.g., also known asprediction horizon, control horizon, or planning horizon). Onlineapproximation of a model over a future horizon only adds furthercomplexity to the online computations for optimization.

Furthermore, the need for distributed optimization further increases thecost of online computations. The need for distributed optimization(e.g., in which a single model is needed by more than one solver) inproblems of realistic complexity has further discouraged any attempt totry to approximate the model online in conventional systems. Theembodiments described herein address this shortcoming by the deploymentof models as servers, enabling a reasonable option to allow distributedoptimization. The possibility of deploying models in the cloud has thepotential to reduce the computational cost of a distributed deployment.

In addition, in a plant-wide/enterprise-wide optimization, there isoften a concern when having a network composed of a large number of unitmodels, complex network connectivity, and a dynamic set of operationalconditions/constraint/objectives. The information used to maintain thisproblem formulation up-to-date typically is obtained from sources thatare distributed throughout the enterprise, and often function with localautonomy. Reliable access to such a large set of measurements/data froma plant floor in a manner that online optimization may be carried outwith confidence is generally perceived as a challenge.

The embodiments described herein address these abovementionedchallenges. In particular, the embodiments described herein enable theuse of distributed parametric hybrid models 58 that are capable ofsecuring proprietary (e.g., protected) information. FIG. 16 is a blockdiagram of a distributed enterprise-integrated parametric hybrid modelenabled control/optimization system 200. The distributedcontrol/optimization system 200 illustrated in FIG. 16 is somewhatsimilar to the control system illustrated in FIG. 7 and described ingreater detail above. However, as illustrated in FIG. 16, in thedistributed control/optimization system 200, the control/optimizationsystem 96 is distributed among several facilities 202, 204. Althoughillustrated in FIG. 16 as including only two facilities 202, 204 (e.g.,a manufacturing facility 202 and an oil and gas facility 204), it willbe appreciated that any number of distributed facilities may communicatewith a centralized decision support server 206, which includes adecision engine 208 that communicates via the communication network 108with a plurality of secure parametric hybrid models 58, each of which islocated at a facility 202, 204.

As will be appreciated, the centralized decision support server 206includes any suitable server hardware (e.g., including memory,processors, and so forth, as described above with respect to thecontrol/optimization system 96) capable of hosting the decision engine208, which is software configured to communicate via the communicationnetwork 108 with the distributed parametric hybrid models 58. Similarly,it will be appreciated that the parametric hybrid models 58 that arelocated at or near their respective facilities 202, 204 will be storedon memory media of model servers 210, 212 physically located at therespective facilities 202, 204. As such, it will be further appreciatedthat the model servers 210, 212 include any suitable server hardware(e.g., including memory, processors, and so forth, as described abovewith respect to the control/optimization system 96) capable of hostingthe parametric hybrid models 58 (e.g., storing instantiated instances ofparametric hybrid model objects that include the parametric hybridmodels 58), each of which are capable of functioning as servers forcommunicating back to the decision engine 208, among other locations, aswell as communicating among each other. Again, it should be noted thatwhile the embodiments described herein are primarily described atutilizing parametric hybrid models 58, in other embodiments, other typesof model objects that include models of a plant or process beingcontrolled may function as secure model-based server objects asdescribed herein.

As illustrated in FIG. 17, each parametric hybrid model 58 (or othertype of model) may be an instantiated software object 214 resident inmemory on its respective model server 210, 212, and may include asoftware interface structure 216 (e.g., IDs 218, attributes 220,services 222, and so forth) configured to communicate with thecentralized decision engine 208 (as well as other devices, softwareobjects, and so forth, communicatively coupled to the communicationnetwork 108) via a predetermined communication protocol 224. As will beappreciated, in certain embodiments, the communication protocol 224 maybe an extension of an existing communication protocol, such as TCP/IP,DeviceNet, Profibus, and so forth, and may define the messages that arecapable of being communicated and understood via devices, softwareobjects, and so forth, that are communicating across the communicationprotocol 224, the types of responses that may be transmitted across thecommunication network 108, how to handle inappropriate communicationsacross the communication network 108, and so forth. As such, thecommunication protocol 224 that is used will be specifically designed tofacilitate the communication of information relating to the parametrichybrid models 58, while keeping proprietary information relating to theparametric hybrid models 58 secure.

It will be appreciated that the graphical modeling tool 82 describedabove with respect to FIG. 7 may be decentralized in operation withrespect to the control/optimization system 200 of FIG. 16. For example,in the embodiment illustrated in FIG. 16, the graphical modeling tool 82may be distributed among the decision support server 206 and the modelservers 210, 212. More specifically, in certain embodiments, thegraphical modeling tool 82 may actually be executed on the centralizeddecision support server 206, and the graphical modeling tool 82 willcommunicate to the distributed parametric hybrid models 58, which arelocated on the distributed model servers 210, 212. When the graphicalmodeling tool 82 is manipulated by a user, the parametric hybrid modelobjects 214 may be instantiated on (or deleted from) their respectivemodel servers 210, 212 in response to command inputs received by thegraphical modeling tool 82 that is executed on the decision supportserver 206. Once instantiated, the parametric hybrid model objects 214may communicate via the communication protocol 224 back to the graphicalmodeling tool 82, enabling distributed operation of the graphicalmodeling tool 82. However, the parametric hybrid model objects 214 willonly communicate across the communication protocol 224 via theinterfaces 216 that are part of the parametric hybrid model objects 214.As such, proprietary information relating to the parametric hybridmodels 58 (that are part of the parametric hybrid model objects 214)will be maintained secure from those that should not have access to it.Although described as being executed on the decision support server 206,it will be appreciated that the graphical modeling tool 82 may insteadbe executed on any of the model servers 210, 212 in other embodiments.Indeed, in certain embodiments, the graphical modeling tool 82 may besplit into several software modules, each of which are executed on theirrespective servers 206, 210, 212 and communicate with each other overthe communication network 108 via the communication protocol 224.

As such, the embodiments described herein enable a computer-implementedmethod whereby a plurality of parametric hybrid model objects 214 areinstantiated on a plurality of model servers 210, 212. Each of theplurality of parametric hybrid model objects 214 includes a parametrichybrid model 58. The one or more decision engines 208 (e.g., one or moredistributed decision engine software modules) may request informationfrom the plurality of parametric hybrid model objects 214 through thecommunication network 108 via the communication protocol 224. Asdescribed above, the parametric hybrid model objects 214 may function asservers, generating responses to the requests for information. Morespecifically, the parametric hybrid model objects 214 may generateapproximations over a future horizon of interest for information thatwould otherwise be generated by their respective parametric hybridmodels 58. These online approximations will ensure that informationtagged (e.g., by the graphical modeling tool 82) as being proprietaryinformation is not sent as part of the responses. Rather, the onlineapproximations will only include information that is not tagged as beingproprietary information. Conversely, the parametric hybrid models 58 mayutilize the proprietary information in generating the onlineapproximations that are sent back to the one or more decision engines208. But, the interfaces 216 of the parametric hybrid model objects 214,in conjunction with the communication protocol 224, do not allow suchproprietary information to be returned as part of the onlineapproximations that are sent to the one or more decision engines 208.

Based at least in part on the information received from the plurality ofparametric hybrid model objects 214, the one or more decision engines208, either individually or collectively as a group of interconnectedsoftware modules, may generate control commands that may be transmittedback through the communication network 108 to at least one of theparametric hybrid model objects 214, where the control commands may beused to control an industrial automation component (e.g., the components102 illustrated in FIG. 7).

It will be appreciated that each of the distributed parametric hybridmodels 58 may communicate with sensors 98 and actuators 100 at therespective facilities 202, 204 as described above with respect to FIG. 7to facilitate optimization and control of the components 102 of thefacilities 202, 204. The use of data-driven probing of secure parametrichybrid models 58 enables efficient approximation of relatively complexdynamic models in substantially real-time. As used herein, the term“substantially real-time” is intended to mean that each successive cycleof optimization calculations and control functions performed by thecontrol/optimization system 200 (or control/optimization system 96) isexecuted almost immediately in succession of one other. In other words,once an iteration of optimization calculations and control functions iscompleted, the next iteration begins. As such, in certain embodiments,the optimization calculations and control functions are performed everysecond, or even more frequently, during operation of the system 10 beingcontrolled. The use of PUNDA modeling techniques as part of theparametric hybrid model architecture offers one computationallyefficient possibility for accurately approximating the relativelycomplex dynamic processes that may be part of the system 10 beingcontrolled. By reasonable extension, any online/real-time probingstrategy of the secure parametric hybrid models 58 that may result in acomputationally workable representation of the secure parametric hybridmodels 58 may also be utilized by the embodiments described herein(e.g., Legendre series, wavelets, support vector machines (SVMs), and soforth).

In addition, the data-driven model approximation of the secureparametric hybrid models 58 is performed over a desiredprediction/planning/control horizon of interest. The approximation modelis generated (e.g., by the decision engine 208) as an approximation ofthe secure content-protected models over a trajectory in substantiallyreal-time. This trajectory may be parameterized as a function of states,inputs, or even time. In such an embodiment, output may be viewed as aspecial case of state.

In addition, the embodiments described herein enable the real-timeapproximation of the secure parametric hybrid models 58 through multiplepotentially distributed decision engines 208. More specifically,although illustrated in FIG. 16 as having only one decision supportserver 206, in other embodiments, multiple distributed decision supportservers 206 having multiple decision engines 208 may be used. As such,the control/optimization system 200 may include both multipledistributed parametric hybrid models 58 and multiple decision engines208 that split the functionality of optimization and control of thesystem. The need for distributed optimization (e.g., in which a singlemodel is needed by more than one solver) in problems of realisticcomplexity has conventionally discouraged any attempt to approximatemodels in real-time. The embodiments described herein overcome suchchallenges insofar as the deployment of parametric hybrid models 58 asservers offers a reasonable option to allow real-time approximation ofthe secure parametric hybrid models 58 for multiple stakeholders in adistributed optimization. An alternative option is to access the secureparametric hybrid models 58 through a cloud, where all the decisionengines 208 are deployed as cloud services. In addition, the secureparametric hybrid models 58 can be deployed as servers either within oroutside the cloud (e.g., depending on the model owner's preferences).

In addition, the embodiments described herein utilize reliable fastaccess to past and current data from the plant floor, MES/businesssystems, and so forth. With advances in data historian and databasetechnology, fast access to massive amounts of plant data and other typesof data can be done very efficiently. The embodiments described hereininclude such data over a prediction/control/optimization horizon in adata historian and/or a database such that the access to the horizondata mirrors that of the access to current and historical data. Theinclusion of the horizon data in a data historian/database significantlyimproves the efficiency of online/real-time approximation of the secureparametric hybrid models 58. In addition, in certain embodiments, thestored data can be encrypted, further securing the parametric hybridmodels 58.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

The invention claimed is:
 1. A control system configured to controloperation of one or more actuators to facilitate performance of anindustrial automation process, wherein: the control system comprises: afirst portion configured to: store a model that models performance ofthe industrial automation process based at least in part on protectedinformation; and determine an approximation of the model that does notinclude the protected information; and a second portion communicativelycoupled to the first portion of the control system, wherein the secondportion of the control system comprises a decision engine configured todetermine a control command to be implemented by the one or moreactuators at least in part by executing a model-based optimization basedat least in part on operating information determined by one or moresensors and the approximation of the model that does not include theprotected information; and the control system is configured to controlperformance of the industrial automation process at least in part byinstructing the one or more actuators to perform the control commanddetermined by the decision engine.
 2. The control system of claim 1,wherein: the first portion of the control system comprises a modelserver; and the second portion of the control system comprises adecision support server.
 3. The control system of claim 1, wherein: thefirst portion of the control system is configured to communicateauthentication information along with the approximation of the model tothe second portion of the control system; and the decision engine isconfigured to authenticate the approximation of the model based at leastin part on the authentication information before using the approximationto execute the model-based optimization.
 4. The control system of claim1, wherein the first portion of the control system is configured tocommunicate authentication information configured to indicate accessrights along with the approximation of the model, wherein theauthentication information is configured to prevent users withinsufficient access rights to access the approximation of the model. 5.The control system of claim 1, wherein the first portion of the controlsystem is configured to be implemented as a first cloud service, thesecond portion of the control system is configured to be implemented asa second cloud service, or both.
 6. The control system of claim 1,wherein the first portion of the control system is configured todetermine the approximation of the model over a future horizon.
 7. Thecontrol system of claim 1, wherein the first portion of the controlsystem is configured to determine the approximation of the model bydetermining a data-driver convex approximation of a trajectory of themodel over a prediction horizon.
 8. The control system of claim 1,wherein the first portion of the control system is configured to modifythe approximation of the model, the model, or both based at least inpart on the control command.
 9. The control system of claim 1, whereinthe first portion of the control system and the second portion of thecontrol system are configured to communicate via an interface protocol,wherein the interface protocol is configured to preclude communicationof the protected information.
 10. The control system of claim 1, whereinthe control system is configured to control performance of theindustrial automation process in an industrial energy system, amanufacturing plant, an industrial plant, a dairy plant, a powder milkdrying process, a power plant, a chemical manufacturing process, an oiland gas process, or a utility plant.
 11. A method of controllingperformance of an industrial automation process, comprising: generating,using a model server of a control system, an approximation of a modelthat models performance of the industrial automation process based atleast in part on protected information, wherein the approximation of themodel does not include the protected information; communicating, usingthe model server, the approximation of the model that does not includethe protected information to a decision support server that determines acontrol command to be implemented by one or more actuators that operateto facilitate performance of the industrial automation process at leastin part by a performing model-based optimization based at least in parton the approximation of the model and operating information related toperformance of the industrial automation process determined by one ormore sensors; and instructing, using the model server, the one or moreactuators to implement the control command to facilitate performance ofthe industrial automation process.
 12. The method of claim 11, whereinthe model server is configured to: indicate access rights configured toprevent a user with insufficient access rights to access theapproximation of the model; and enable the decision support server toauthenticate the approximation of the model before using theapproximation to perform the model-based optimization.
 13. The method ofclaim 11, comprising communicating, using the model server, theapproximation of the model to a cloud based service, wherein the cloudbased service is configured to implement the decision support server.14. The method of claim 11, wherein determining the approximation of themodel comprises determining a data-driven convex approximation of atrajectory of the model over a prediction horizon.
 15. The method ofclaim 11, comprising modifying, using the model server, the model, theapproximation of the model, or both based at least in part on thecontrol command.
 16. A tangible, non-transitory, computer-readablemedium that stores instructions executable by one or more processors ofa control system, wherein the instructions comprise instructions to:determine, using the one or more processors, an approximation of a modelthat models performance of an industrial automation process based atleast in part on protected information, wherein the approximation of themodel does not include the protected information; perform, using the oneor more processors, a model-based optimization based at least in part onoperating information related to performance of the industrialautomation process measured by one or more sensors and the approximationof the model that does not include the protected information todetermine a control command to be implemented by an actuator thatoperates in the industrial automation process; and instruct, using theone or more processors, the actuator to implement the control command tofacilitate performance of the industrial automation process.
 17. Thecomputer-readable medium of claim 16, comprising instructions to:determine, using the one or more processors, an expected result of theactuator implementing the control command based at least in part onexecution to the model-based optimization; instruct, using the one ormore processors, an electronic device to present the expected result toa user to enable the user to provide user inputs to the control systembased at least in part on the expected results; and receive, using theone or more processors, a user input to the control system instructingimplementation of the control command, wherein the instructions toinstruct the actuator to implement the control command compriseinstructions to instruct the actuator to implement the control commandwhen the user input is received.
 18. The computer-readable medium ofclaim 16, comprising instructions to authenticate, using the one or moreprocessors, the approximation of the model before the approximation ofthe model is used to execute the model-based optimization.
 19. Thecomputer-readable medium of claim 16, wherein the approximation of themodel comprises a data-driven convex approximation of a trajectory ofthe model over a prediction horizon.
 20. The computer-readable medium ofclaim 16, comprising instructions to modify, using the one or moreprocessors, the model, the approximation of the model, or both based atleast in part on the control command.